The reciprocal associations between catastrophizing and pain outcomes in patients being treated for neuropathic pain: a cross-lagged panel analysis study
Bibliographic record
Abstract
Catastrophizing is recognized as a key psychosocial factor associated with pain-related negative outcomes in individuals with chronic pain. Longitudinal studies are needed to better understand the temporal relationship between these constructs. The aim of this study was to determine if changes in catastrophizing early in treatment predicted subsequent changes in pain intensity and interference later in treatment, or alternately, if early changes in pain intensity and interference predicted subsequent changes in catastrophizing. A total of 538 patients with neuropathic pain were recruited from 6 multidisciplinary pain clinics across Canada. Study participants were asked to complete measures of catastrophizing, pain intensity, and interference when first seen in the clinic and then again at 3- and 6-month follow-ups. Cross-lagged panel analyses were used to determine the temporal associations among the study variables. The results showed that decreases in catastrophizing early in treatment prospectively predicted improvement in both pain intensity and interference later in treatment. Converse temporal relationships were also found, where a reduction in pain intensity and interference early in treatment predicted a subsequent diminishing of catastrophizing. All 4 unique cross-lagged correlations significantly accounted for an additional 4% to 7% of the total variance. The findings are consistent with theoretical models hypothesizing a causal impact of catastrophizing on pain, suggesting a mutual causation between these factors. The results support that treatments targeting catastrophizing may influence other pain-related outcomes, and conversely that treatments aiming to reduce pain could potentially influence catastrophizing. There may therefore be multiple paths to positive outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".